Nonparametric Face Verification Using a Novel Face Representation

We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors [1]. Our LARK descriptor measures a self-similarity based on “signal-induced distance” between a center pixel and surrounding pixels in a local neighborhood. By applying principle component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state of the art face verification performance on the challenging benchmark “Labeled Faces in the Wild” (LFW) dataset [2]. In the case where training data are available, we employ oneshot similarity (OSS) [3], [4] based on linear discriminant analysis (LDA) [5]. The proposed approach achieves state of the art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates) respectively as a single descriptor representation, with no preprocessing step. As opposed to [4] which combined 30 distances to achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity.